How to Approach GMAT Data Insights Like a Pro: From Basics to Advanced Techniques

The GMAT Data Insights section was introduced in 2025 as part of the new GMAT Focus Edition, a redesigned version of the traditional GMAT exam. This section was developed to reflect the evolving demands of graduate business education, particularly the growing emphasis on data-driven decision-making. Data Insights occupies a full one-third of your overall GMAT score, underscoring its importance in the admissions process. With 20 questions to be answered in 45 minutes, this section is time-pressured, cognitively demanding, and strategically unique. It blends verbal and quantitative skills, requiring test-takers to think critically, interpret data visualizations, evaluate information from multiple sources, and assess the sufficiency of evidence. For many students, this section feels like a departure from traditional test formats. It is more applied, more integrated, and less about memorized formulas and more about active reasoning.

The structure of the Data Insights section is both consistent and complex. You are presented with five main types of questions: Multi-Source Reasoning, Table Analysis, Graphics Interpretation, Two-Part Analysis, and Data Sufficiency. Each of these types presents different challenges. Some focus on reading and synthesizing written information, others on interpreting visual representations, and others still on evaluating logical or mathematical sufficiency. The exact distribution of these types may vary from test to test, but they generally fall within expected ranges. Graphics Interpretation, for instance, typically comprises 20 to 30 percent of the section, while Data Sufficiency makes up as much as 40 percent. A key feature of the section is that you must answer one question to move on to the next, and you cannot go back once a question has been submitted. This structure heightens the stakes of every individual decision, placing a premium on both accuracy and confidence.

What makes the Data Insights section distinct is not just the types of questions but the cognitive approach it requires. Unlike the more isolated skills tested in traditional verbal or quant sections, Data Insights forces you to engage with integrated reasoning. This means you must move between textual descriptions, numerical data, and visual representations, often within a single question. The ability to switch gears rapidly, prioritize information effectively, and maintain a high level of focus under time pressure is critical. Many test-takers initially struggle not because the content is especially advanced, but because the mental transitions are unfamiliar. You might go from evaluating a logical argument, to interpreting a scatter plot, to assessing whether a pair of statements can determine the value of a variable, all within a few minutes. Developing comfort with these shifts is a major part of successful preparation.

Each question in Data Insights may involve one or more sub-questions. For example, Multi-Source Reasoning problems typically present a prompt with three information sources and ask you to answer two or three related questions. Similarly, Table Analysis problems often come with three dichotomous-choice items, such as true/false or yes/no. What’s crucial to understand is that no partial credit is awarded. If you answer any subpart of a question incorrectly, you receive no credit for the entire item. This format requires you to treat every part of every question with equal care. Rushing through one component can negate the effort you put into another. As a result, time management becomes an especially delicate balancing act. You must work quickly enough to complete the section but carefully enough to avoid careless errors that can have disproportionate consequences.

Another layer of complexity in the Data Insights section lies in its reliance on real-world-style reasoning. The questions are not meant to test abstract knowledge in isolation but your ability to interpret and analyze practical information. This reflects the kinds of challenges MBA students face in their coursework and in business settings. For instance, you may be asked to interpret a business scenario, analyze competing projections, assess risks, or determine which pieces of evidence support a particular conclusion. The skill set required here goes beyond academic reasoning. It includes executive function skills such as filtering relevant information, evaluating trade-offs, and making decisions with incomplete data. These are competencies that business schools value highly, which explains why this section has become a cornerstone of the revised GMAT.

Time pressure in Data Insights cannot be overstated. With 20 questions to answer in 45 minutes, you have an average of just over two minutes per question. However, because some question formats include multiple sub-questions, the effective time per decision can be even shorter. You must develop an internal clock that helps you allocate your time wisely without second-guessing or hesitating. Some questions may be completed quickly, while others demand closer attention. A successful test-taker learns how to balance the overall section, spending less time on familiar or straightforward questions to reserve more time for the complex or data-heavy ones. Timed practice is essential for developing this skill. Untimed drills may help you understand question formats, but only timed practice can prepare you for the cognitive stress of the live exam.

Visual literacy is another essential skill for the Data Insights section. Several question types rely heavily on interpreting visual data, including charts, graphs, and tables. These visuals are not decorative; they are integral to understanding the question and identifying the correct answer. Misreading a single axis, overlooking a unit of measurement, or failing to understand a sorting feature can lead to incorrect conclusions. Developing strong visual literacy means more than just recognizing graph types. It means being able to quickly orient yourself to new visuals, identify trends and outliers, and extract relevant data points. Regular exposure to visual data, in both practice problems and real-world materials like financial newspapers and reports, can help you improve this skill and approach visual-heavy questions with greater confidence.

Despite its challenges, the Data Insights section offers opportunities for test-takers who approach it strategically. One advantage is that the section draws on skills that can be actively developed through targeted practice. You do not need a background in advanced mathematics or business analytics to succeed. What you need is a structured approach to learning the formats, practicing efficiently, and developing cognitive endurance. Many students find that their performance improves significantly over time as they become more familiar with the demands of the section. This improvement comes not only from solving more questions but also from developing better heuristics, or mental shortcuts, for filtering information and identifying the most relevant pieces of data.

Familiarity is one of the greatest assets you can bring to the Data Insights section. Every question type has a characteristic structure, and learning to recognize these structures quickly can save valuable seconds on test day. For example, knowing that a Table Analysis problem always uses a sortable table with dichotomous-choice questions helps you navigate the visual and understand what kind of logic is required. Similarly, understanding that Multi-Source Reasoning questions require toggling between different sources prepares you to extract information systematically. The more you practice with full-format problems, the more you train your brain to anticipate these structures and respond effectively.

A final but crucial element of success in the Data Insights section is mindset. This section is designed to feel overwhelming. The volume of information, the variety of formats, and the unforgiving timing all contribute to a sense of cognitive overload. But this is not a flaw in the design. It is intentional. The GMAT wants to see how well you handle complex information under pressure. The best test-takers are not those who never feel overwhelmed but those who learn to manage their focus, stay calm, and continue working through the problem in front of them. Developing this kind of resilience takes time and practice. Early in your prep, you may feel scattered or confused. That’s okay. Over time, as your familiarity grows and your confidence builds, you will find yourself approaching the questions with greater clarity and control.

Breaking Down the Five GMAT Data Insights Question Types

To master the GMAT Data Insights section, you must become familiar with the five question types that make up this part of the test. Each one tests a slightly different set of skills, and each one introduces its own format, logic structure, and traps. Understanding the unique characteristics of these question types is essential for developing efficient strategies and avoiding preventable mistakes. While the content may seem diverse, there is a consistent emphasis throughout the section on information evaluation, pattern recognition, and evidence-based decision-making. Let’s examine each question type in turn, with attention to what makes them distinct, what skills they demand, and how to approach them effectively.

The first type, Multi-Source Reasoning, challenges your ability to synthesize and compare data from different sources. On screen, you are presented with a split layout. On the left side are three clickable tabs, each containing a different piece of information. These could be brief emails, memos, research excerpts, data tables, or other textual or visual materials. You can only view one tab at a time. On the right side of the screen are either standard five-choice multiple-choice questions or multiple dichotomous-choice questions, where each row asks a yes/no or true/false judgment. The key difficulty with Multi-Source Reasoning is navigating between tabs, keeping track of where relevant information is located, and not getting lost in details. Since each piece of information is typically relevant to one or two questions, it’s crucial to approach the problem with a clear plan: identify what each source covers, understand the question being asked, and locate the specific information without rereading everything. It helps to skim the content of each tab before diving into the questions, building a quick mental map of the layout.

Table Analysis questions focus on your ability to work with large datasets. You are presented with a table that can be sorted by each column and a set of dichotomous-choice questions asking you to categorize information—for example, labeling statements as true or false or as meeting or not meeting a particular condition. The volume of data can be overwhelming at first glance. One of the most important skills in Table Analysis is filtering: you must ignore irrelevant columns, understand what sorting does and does not reveal, and focus your attention on the specific relationships being tested. While sorting is a powerful tool, it can also be a time trap if used indiscriminately. You should sort the table only when necessary and with a specific hypothesis in mind. For instance, if a question asks which products have above-average profitability, you might sort the table by the profitability column to group similar values together, making it easier to identify patterns. Just as important is a careful reading of the prompt. Some questions may use subtle distinctions or conditionals in their phrasing, such as “only if” or “at least one of.” Misinterpreting such wording can result in completely misclassifying the data.

Graphics Interpretation is arguably the most visual question type in the Data Insights section. You are presented with a graph, chart, or diagram, along with a two-part question. Each part is a sentence with a blank space and a drop-down menu. Your task is to select the correct value or category from each menu based on the information in the graphic. The range of visuals you might see includes bar charts, pie charts, scatterplots, line graphs, and unusual formats such as bubble charts or flow diagrams. Understanding the axes, units, legends, and data categories is vital. Many errors in Graphics Interpretation arise from careless reading of the axes or failing to grasp how the visual represents proportion, trend, or scale. For example, a common mistake is to assume that a pie chart is showing absolute values when it may be displaying percentages or proportions. A successful strategy is to take ten seconds at the beginning to orient yourself: read the labels carefully, identify what variables are being shown, and locate any embedded totals or reference lines. Only after that should you begin to interpret the drop-down options and the language in the sentence. Sometimes the sentence itself is the hardest part—it may use complex phrasing or implicit assumptions. Read each sentence slowly and test each drop-down option against your understanding of the graph to ensure that your selection is supported by the data.

Two-Part Analysis questions return to a more traditional logical reasoning format, but with an integrated twist. You are given a short passage and a table with two columns for answers. Your job is to select one response in each column so that both are correct in relation to the prompt. The relationship between the two parts may be mathematical, logical, or strategic. Sometimes the two questions are parallel—both asking about different consequences of a scenario. Other times, they are interdependent—where your answer to one part influences the answer to the other. The key difficulty with Two-Part Analysis is maintaining the relationship between the two parts in your head while evaluating multiple options. If you treat each column independently, you may choose answers that make sense on their own but not in combination. Therefore, it’s important to scan both columns before selecting any answer and to think in terms of pairs, not individual answers. A useful technique is process of elimination. Often, you can quickly eliminate one or two answer choices that clearly don’t fit. From there, test potential pairs by checking them against the logic or math in the prompt. Two-Part Analysis often shows up in problems involving strategy (e.g., cost-benefit decisions), coordination (e.g., task assignment), or simultaneous equations. Practice problems in this format help sharpen your ability to reason in pairs, track conditional logic, and avoid selecting mismatched options.

The final and perhaps most challenging question type in the section is Data Sufficiency. This format is familiar from the Quantitative Reasoning section, but its presence in Data Insights means you may encounter it in a more applied, business-oriented context. In each Data Sufficiency question, you are given a question followed by two statements. Your task is not to solve the question but to assess whether each statement alone or together provides enough information to answer it. The five answer choices are always the same, and memorizing them is essential: (A) statement 1 alone is sufficient, (B) statement 2 alone is sufficient, (C) both statements together are sufficient, (D) each statement alone is sufficient, and (E) neither is sufficient. Data Sufficiency requires a very specific kind of thinking. You must separate the question of sufficiency from the question of calculation. Many test-takers go astray by trying to solve the question when all they need to do is assess whether a solution is possible. One of the most powerful techniques for Data Sufficiency is the use of smart examples. By plugging in values or testing edge cases, you can often determine whether a statement narrows the possibilities enough to answer the question. Equally important is the discipline to stop once sufficiency is confirmed—you don’t need to find the actual answer.

Within Data Sufficiency, you’ll encounter two main subtypes: value questions and yes/no questions. Value questions ask whether the information is enough to determine a specific value. Yes/no questions ask whether the information is enough to consistently answer yes or no. This distinction matters because the logic of sufficiency changes depending on the question type. For value questions, ambiguity is the enemy—if multiple values are possible, the statement is not sufficient. For yes/no questions, consistency is key—if a statement sometimes leads to yes and sometimes to no, it is not sufficient. Knowing this subtle difference can help you avoid common traps. Many yes/no questions include variables with multiple conditions, such as inequalities or absolute values. In such cases, testing both positive and negative examples can clarify whether sufficiency is truly achieved.

Understanding these five question types—Multi-Source Reasoning, Table Analysis, Graphics Interpretation, Two-Part Analysis, and Data Sufficiency—is fundamental to success in the Data Insights section. But mastery doesn’t come from reading descriptions alone. You need to engage with each type in practice, internalize their logic, and build your timing instincts. Each format has its own rhythm and tempo. Some allow you to skim and scan, others demand deep focus and structured note-taking. By customizing your approach to each type and learning how to shift gears mentally, you’ll begin to handle the section more fluidly and confidently.

Time Management and Strategic Thinking for Data Insights Success

One of the most challenging aspects of the GMAT Data Insights section is not simply understanding the question types, but navigating them under time pressure. With 20 questions to be completed in 45 minutes, that gives you an average of just over two minutes per question. However, because many prompts contain multiple components or sub-questions, and some formats are more time-consuming than others, sticking to a strict time-per-question model isn’t always the most efficient strategy. Instead, time management in the Data Insights section demands flexibility, awareness of question structure, and a strategic mindset. In this part, we will focus on how to approach the timing of the section, what strategies help maintain performance under pressure, and how to prepare your decision-making process to stay sharp even as the test becomes increasingly complex.

The first principle of time management is recognizing that not all questions take equal time. Multi-source reasoning and table analysis questions tend to require more reading and navigation than other types. Graphics interpretation, while visual, can often be quicker if the chart or graph is straightforward. Two-part analysis problems, although cognitively demanding, can be completed efficiently if the logic is clear. Data sufficiency questions, depending on their complexity, may be short or long, depending on whether the sufficiency is obvious or nuanced. That means that part of your job as a test taker is to quickly assess the time load of a question upon reading it. Within the first 15 seconds of encountering a problem, you should be able to tell whether it’s likely to demand a full two minutes or whether it’s something you can answer in 60 to 90 seconds.

Another key concept in GMAT Data Insights timing is the cost of indecision. Because you cannot return to questions after answering them, hesitation becomes expensive. If you spend three minutes waffling between options and still get the answer wrong, you’ve not only lost the point—you’ve lost time that could have been used to score on a simpler question later. For this reason, you should develop what many test experts refer to as a timing cutoff. If you’re past the two-minute mark and you still don’t have a clear answer pathway, it’s often better to make your best guess and move on. The GMAT is not a perfection test; it’s an efficiency test. Your ability to make a good enough decision quickly is far more valuable than your ability to struggle toward the right answer slowly.

Let’s talk about pacing strategies. A good way to start is by aiming to complete the first 10 questions in roughly 21 to 22 minutes. That gives you a bit more time for the second half, which often feels harder because of fatigue. However, you must avoid the trap of rushing through the first few questions just to “bank” time. If you answer those questions poorly, you lose easy points and shake your confidence. Instead, aim to move steadily and confidently, answering the earlier problems with care but without second-guessing. You can afford to spend a little extra time on a multi-part question if you’re confident that you understand the structure. But be wary of questions with three or more rows of true/false options—those tend to be more difficult and may be better candidates for time-saving strategies.

Among the most helpful timing tools is a mental clock. Every few questions—perhaps every 5—you should glance at the test timer to make sure you’re on track. If you’re already behind, that doesn’t mean you should start rushing, but you should start tightening up your time decisions. That might mean skipping detailed reading of answer choices, narrowing the field to two possibilities and making a quick choice. If you’re ahead of pace, use that time to stay composed rather than overanalyzing answers. Extra time is a cushion, not an invitation to second-guess. Mental check-ins like this help keep panic at bay, especially when a tough question appears unexpectedly.

Another vital area of strategy is error prevention. Because the Data Insights section requires quick interpretation of visuals, conditional logic, and numerical precision, small mistakes can cost you full credit. In fact, for multi-part questions, if you answer even one part incorrectly, you lose all points for that question. That means accuracy matters enormously. But how do you maintain accuracy without burning through your time? One solution is to build habits of active reading. This means underlining key information with your eyes, rephrasing question stems in your head, and double-checking units and conditions. For instance, if a table shows profit in thousands, and a question asks for total revenue, forgetting to adjust your units could lead to a wildly incorrect answer. Slowing down slightly to confirm these details can actually save time in the long run by preventing errors that would shake your confidence and force re-evaluation.

Another powerful tool is pattern recognition. Many Data Insights questions follow similar formats and logic. For instance, multi-source reasoning often includes one tab with irrelevant or misleading information. Graphics interpretation often uses comparative structures, such as increases vs. decreases. Table analysis frequently tests your ability to distinguish between absolute and relative differences. Once you recognize these patterns, you can preempt certain traps and move faster. For example, if you know a typical graphic question involves percentages hidden in bar charts, you’ll look for totals right away. If you know that table analysis questions often hinge on sorting, you’ll assess which column to sort before even beginning your calculations. These mental shortcuts are only possible through practice. But once learned, they become automatic, saving you vital seconds on test day.

Let’s also consider the mindset aspect of strategic thinking. Unlike the Quantitative or Verbal sections, Data Insights requires frequent switching between different skills—numerical reasoning, verbal analysis, visual interpretation. This mental shifting can be exhausting. The best test takers develop what might be called a modular mindset. They treat each question type as its own module with its own logic and rules. Before beginning a new question, take one second to reset your thinking. If you’ve just completed a table question and now see a bar chart, don’t dive in immediately. Pause, breathe, recognize the change in format, and adjust your approach. This kind of mental boundary-setting prevents confusion and keeps your thinking structured.

Practicing strategic decision-making also involves knowing when to be bold. Some questions are deliberately confusing. They include unnecessary information, ambiguous phrasing, or distractors. If you spend too much time trying to untangle a trick question, you may fall behind. This is where intuition, built from practice, plays a key role. Sometimes you’ll sense that a question is trying to trap you. In those moments, it may be better to make an informed guess than to walk into the trap. For example, if a data sufficiency question includes a redundant statement that seems too neat, it may be disguising insufficiency. If your gut tells you the logic doesn’t hold, don’t talk yourself into an answer that doesn’t make sense.

Visual fatigue is also an issue, particularly with charts and graphs. Your eyes may glaze over or misread a trend after seeing a dozen visuals in quick succession. To combat this, develop a quick checklist for graphics: check axes, note units, read labels, look for outliers. This creates consistency in how you process visuals and reduces the cognitive load each time. Similarly, for data tables, train yourself to immediately spot key columns and look for sorting cues. If a question mentions “highest increase,” “greatest margin,” or “lowest value,” your first move should be to find the relevant column. These moves reduce hesitation and sharpen focus.

Finally, preparation should include realistic simulation of the testing environment. Too many test takers practice with untimed sets or review questions one at a time. This does not prepare the brain for the cognitive juggling required on the actual exam. You should regularly practice full 45-minute sections under test conditions: no breaks, no review until the end, and no skipping. Only this kind of practice reveals your true pacing issues and helps you build the endurance necessary for consistent performance. After each timed set, review not just what you got wrong, but how long you spent on each question and whether the result justified the time spent. If a correct answer took four minutes, it may be a hidden weakness. If a wrong answer took 30 seconds, it might be a reckless guess.

In sum, the GMAT Data Insights section is a test of strategy as much as of content. Success comes from planning your time, recognizing question patterns, controlling your pace, and staying sharp across diverse problem types. In the final part of this guide, we’ll explore how to build those skills through smart preparation—including specific exercises, reading habits, and review methods that help internalize the section’s core logic and develop the calm, confident mindset that high scorers rely on.

Building Long-Term Mastery of Data Insights Through Smart Preparation

While understanding question types and mastering time management are essential for succeeding on the GMAT Data Insights section, these efforts must be supported by a deliberate and well-structured preparation strategy. Simply doing a few practice problems here and there will not lead to meaningful improvement. To truly excel in the Data Insights section, your preparation should be comprehensive and strategic, targeting both skill development and test-day execution. In this final part, we’ll examine how to build a disciplined preparation routine, what kinds of resources and exercises are most effective, how to evaluate your own progress, and how to maintain peak performance in the final weeks before the exam.

To begin building mastery, you need to understand the unique blend of abilities that the Data Insights section tests. These include numerical reasoning, logical deduction, comparative analysis, visual literacy, attention to detail, and reading comprehension. No single practice format targets all of these at once. Therefore, your preparation should consist of both focused drills and full-section simulations. For instance, you might begin your studies by isolating one question type—such as graphics interpretation—and working through a dozen examples. This allows you to build familiarity with typical formats and data presentation styles. Once you’ve developed accuracy in one type, move to another, gradually building fluency across all five categories. In parallel, start practicing full-length Data Insights sets under timed conditions at least once per week.

Another critical pillar of preparation is reviewing mistakes. Many students review their errors in a superficial way, simply noting the correct answer and moving on. This does little to improve performance. Instead, every mistake should be treated as a diagnostic opportunity. Ask yourself three things: Why did I get it wrong? What pattern or assumption led me there? And what will I do differently next time? For example, if you missed a multi-source reasoning question because you trusted the first tab without comparing it to the others, you should adopt a habit of always previewing all tabs before answering. This process of reflective correction rewires your approach and prevents repeated errors. Similarly, when you get a question right, but it took you too long or you were unsure, treat it as an inefficiency. Efficiency is just as important as correctness in the Data Insights section.

A highly effective study tool in this process is an error log. This is a simple document where you record every question you miss or struggle with, along with a brief explanation and a note about what skill or concept was involved. Over time, you will notice patterns in your performance. Perhaps you frequently misread graph axes, or maybe you struggle with conditional reasoning in table analysis questions. Once these patterns emerge, you can target them with focused review. Your error log becomes a map of your weaknesses, and by addressing them directly, you convert your preparation time into measurable improvement.

Beyond practice problems and review, you should engage in skill development outside the GMAT. Because the Data Insights section is designed to mimic real-world decision-making, you can improve your performance by reading materials that simulate that type of reasoning. Consider reading financial reports, data-rich news articles, or academic research summaries. Outlets that frequently use data visualizations—such as business newspapers and policy journals—can help you become more comfortable with charts, tables, and argument structures. As you read, practice interpreting the visuals on your own before reading the captions or explanations. Ask yourself what the data suggests, what the underlying assumptions are, and what potential flaws exist in the analysis. Over time, you will train yourself to engage critically and efficiently with complex data—exactly the kind of thinking the GMAT expects.

One commonly overlooked part of preparation is learning to manage stress. Data Insights questions are often dense and unfamiliar, and it’s easy to panic when a graph looks confusing or when a prompt seems overwhelming. The best way to reduce this anxiety is to simulate pressure in your practice. Set a timer, remove all distractions, and complete a full 45-minute section without pause. Afterward, take a few minutes to reflect on your emotional state. Did you feel rushed? Did your confidence dip after a tough question? Did you waste time second-guessing yourself? This kind of self-assessment helps build resilience. The more you expose yourself to test-like conditions, the more comfortable and calm you’ll be on the actual exam.

In the final weeks before your test date, shift your focus from skill acquisition to execution. This is the time to build consistency. Create a schedule that includes a mix of review, timed practice sets, and full-length mocks. Avoid cramming or adding new techniques at this stage. Instead, trust the strategies you’ve refined over weeks of study. Focus on reinforcing your habits: preview all tabs before answering, analyze all axes on graphs, evaluate sufficiency before solving equations, double-check table columns before drawing conclusions. These habits should become second nature by test day. Just as importantly, prioritize rest and routine in the days leading up to the exam. Your brain performs best when it is well-rested, nourished, and calm.

Another useful tool during this final phase is self-coaching. Before each practice session, write down a brief intention. For example: “Today I will focus on maintaining pace through multi-part questions,” or “I will resist overanalyzing difficult prompts.” After the session, evaluate how well you followed through. This kind of self-direction keeps your practice intentional rather than reactive. It also trains you to maintain focus during high-stakes moments, such as the real test.

Finally, remember that Data Insights is not a section you can fake your way through. Because many questions rely on multi-step reasoning, random guessing rarely results in correct answers. Success in DI comes from preparation, not luck. But the good news is that the section rewards consistency and pattern recognition. Once you internalize the logic of each question type and develop habits of efficient interpretation, you’ll find that the section becomes far more predictable. That predictability is your ally. By test day, you should aim to have a clear approach for each question type, a well-practiced timing strategy, and a calm mindset that allows you to stay focused from the first question to the last.

In conclusion, mastering the GMAT Data Insights section requires more than just familiarity with question types. It demands rigorous, reflective preparation that builds both skill and strategy. By practicing deliberately, reviewing mistakes in depth, simulating real test conditions, and sharpening your ability to make decisions under pressure, you prepare not only to succeed in this section but to build the kind of analytical mindset that the GMAT—and business school—ultimately values. The Data Insights section may seem daunting at first, but with the right preparation, it becomes not just manageable, but an opportunity to demonstrate your readiness for complex, data-driven decision-making.

Final Thoughts 

The GMAT Data Insights section is a unique blend of quantitative reasoning, verbal interpretation, logical analysis, and data fluency—skills that MBA programs believe are essential for modern business leaders. As the newest addition to the exam, introduced in 2023, it represents a shift in how management potential is evaluated. No longer is it sufficient to just solve math problems or decode arguments. The real test is in your ability to synthesize diverse data, draw reasoned conclusions, and stay efficient under pressure. And while this might sound intimidating at first, it’s also a great opportunity—because most test-takers struggle not from lack of intelligence, but from lack of preparation strategy.

The key takeaway is that success in Data Insights doesn’t come from memorizing formulas or mastering isolated question types in a vacuum. It comes from understanding the logic behind the section, recognizing how different formats test similar skills, and practicing in ways that sharpen your ability to think quickly and clearly. From multi-source reasoning to data sufficiency, each question is a chance to prove that you can work through ambiguity, weigh evidence, and arrive at justified conclusions. These are skills you will use in every case discussion, strategy session, and analytics presentation in business school and beyond.

As you prepare, keep the big picture in mind. It’s not about perfect performance every time—it’s about steady improvement. Track your progress, identify your weak spots, and keep refining your approach. Remember that every mistake is a step toward better performance, and that your ultimate goal isn’t just a high score—it’s developing the kind of critical thinking that business schools and employers respect. The time you spend mastering Data Insights will pay dividends long after test day, in classrooms, boardrooms, and beyond.

Approach your prep with curiosity and discipline. Learn from each attempt. Push yourself to think a little faster, read a little more carefully, and analyze with more precision. With consistent effort, you’ll find that what once felt overwhelming starts to feel familiar—and even intuitive. When test day arrives, you’ll be ready not just to take on the Data Insights section, but to use it as a platform to show just how capable you really are.